RAG Development Services
RAG that retrieves the right answer — not just an answer.
Retrieval-Augmented Generation built for accuracy, speed, and enterprise-grade reliability.
18 years of data architecture experience behind every pipeline. We've built RAG systems processing millions of documents for legal, healthcare, and financial clients — response accuracy above 95%, query latency under 2 seconds, in production.
Why RAG matters
Your knowledge base is your competitive advantage. RAG makes it accessible.
Every organisation has proprietary knowledge locked in documents, databases, and systems that employees can't search effectively. RAG connects language models to your specific data — delivering accurate, cited answers grounded in your actual knowledge, not the model's training data.
accuracy on production RAG systems — compared to ~60% for ungrounded LLMs
AurvikAI benchmark data
Grounded in your data
Answers sourced from your documents, policies, and databases — not the model's general knowledge.
Cited and verifiable
Every response includes source citations that users can verify — building trust in AI-generated answers.
Always current
New documents are indexed automatically — no model retraining required when your knowledge changes.
Enterprise-grade security
Access controls ensure users only retrieve documents they're authorised to see.
How AurvikAI builds RAG systems
A five-phase methodology that prioritises retrieval quality above all else.
Design the knowledge architecture
The retrieval layer is the hardest part of RAG. We design chunking strategy, embedding model selection, vector store architecture, and metadata schema before writing any code. Getting this right determines everything.
Build and evaluate the retrieval pipeline
We evaluate retrieval quality before connecting it to a language model — using precision, recall, and mean reciprocal rank against a curated evaluation set. A RAG system is only as good as its retrieval.
Implement hybrid search and re-ranking
Pure vector search misses exact matches. Pure keyword search misses semantic similarity. We implement hybrid search combining dense and sparse retrieval, with re-ranking tuned to your query distribution.
Connect the generation layer
Context window management, citation generation, and system prompt design are implemented once retrieval quality meets the target threshold. The language model is the final step, not the first.
Deploy with freshness monitoring
RAG systems degrade when the knowledge base goes stale. We build document update pipelines, freshness monitoring, and re-indexing triggers that keep the system accurate as your documents change.
Beyond basic RAG
Advanced RAG architectures for enterprise scale.
Basic RAG — chunk documents, embed them, retrieve the top 5, pass to an LLM — works for demos. Production RAG requires hybrid search, re-ranking, context window management, citation tracking, and access-controlled retrieval. We build the version that survives enterprise scale.
Most RAG implementations fail because the retrieval layer is an afterthought. We design the knowledge architecture first — chunking strategy, embedding models, vector store selection — before a single query is written.
Improvement in retrieval precision vs. basic RAG
Query-to-cited-answer latency
RAG applications we've built
From legal research to customer support — production systems processing millions of documents.
Making your organisation's collective knowledge searchable and accessible through natural language.
Employees find answers from company policies, procedures, and documentation in seconds instead of hours.
Developers query API docs, architecture decisions, and runbooks with natural language.
New employees get accurate answers about processes, benefits, and tools — sourced from official documents.
Research teams search across papers, patents, and internal reports with semantic understanding.
The difference RAG makes
Generic LLMs hallucinate. RAG systems ground every answer in your verified knowledge.
Without RAG
- LLMs generate plausible-sounding answers from training data — not your data
- No citations or source verification for AI-generated responses
- Knowledge locked in documents that employees search manually for hours
- Model retraining required every time your knowledge base changes
- No access controls — the model knows everything or nothing
With AurvikAI RAG
- Every answer grounded in your specific documents with verifiable citations
- Source links included in every response for user verification
- Natural language access to your entire knowledge base in seconds
- New documents indexed automatically — no retraining required
- Role-based access controls ensuring users see only authorised content
AurvikAI engineering team building a multi-million document RAG pipeline
Common questions about RAG development
From teams evaluating RAG for their knowledge management and AI applications.
Context window limits mean you can't give the LLM your entire knowledge base. RAG retrieves the specific relevant content for each query — so the model works with the most relevant information. RAG also enables citation, freshness without retraining, and access-controlled retrieval that context stuffing cannot provide.
RAG proof of concept
See RAG working on your own documents in 2 weeks.
We'll build a proof of concept using a sample of your actual documents — so you can evaluate RAG quality on your real queries before committing to a full implementation.
INSIGHTS
Thinking worth reading
Ready to make your knowledge base intelligent?
Let's start with your documents and your use case. We'll show you what RAG can deliver — with real retrieval quality metrics, not a generic demo.